Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,245
2	New Jersey	19,109
3	Rhode Island	15,606
4	Massachusetts	15,587
5	District of Columbia	14,302
6	Connecticut	12,873
7	Delaware	11,139
8	Louisiana	11,122
9	Illinois	10,991
10	Maryland	10,851
11	Nebraska	9,352
12	Iowa	8,389
13	Arizona	8,022
14	Mississippi	7,693
15	South Dakota	7,181
16	Virginia	6,911
17	Michigan	6,846
18	Pennsylvania	6,817
19	Indiana	6,530
20	Alabama	6,342
21	Georgia	6,082
22	Minnesota	5,940
23	Utah	5,756
24	Arkansas	5,526
25	Colorado	5,361
26	Tennessee	5,274
27	North Carolina	5,227
28	New Mexico	5,168
29	South Carolina	5,168
30	California	4,834
31	Florida	4,818
32	Nevada	4,564
33	Wisconsin	4,378
34	North Dakota	4,364
35	Kansas	4,345
36	Texas	4,301
37	New Hampshire	4,097
38	Washington	4,041
39	Ohio	3,946
40	Kentucky	3,205
41	Missouri	3,119
42	Oklahoma	2,786
43	Idaho	2,468
44	Maine	2,227
45	Wyoming	2,166
46	Puerto Rico	2,093
47	Vermont	1,865
48	Oregon	1,726
49	West Virginia	1,446
50	Alaska	1,237
51	Montana	695
52	Hawaii	569

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	375
2	Mississippi	210
3	South Carolina	183
4	Arkansas	169
5	Louisiana	158
6	Florida	150
7	Texas	150
8	California	136
9	Utah	133
10	Georgia	122
11	Nevada	116
12	Alabama	105
13	North Carolina	101
14	Iowa	96
15	Tennessee	87
16	Oklahoma	83
17	Idaho	75
18	Nebraska	66
19	New Mexico	64
20	Rhode Island	61
21	Virginia	60
22	Minnesota	59
23	Maryland	57
24	Washington	57
25	Delaware	56
26	Missouri	53
27	Ohio	53
28	District of Columbia	51
29	Kansas	50
30	Oregon	48
31	South Dakota	48
32	Wisconsin	48
33	Illinois	45
34	Wyoming	43
35	Indiana	41
36	Pennsylvania	41
37	Kentucky	39
38	New Jersey	33
39	Colorado	31
40	New York	31
41	North Dakota	30
42	Alaska	27
43	Massachusetts	24
44	Puerto Rico	23
45	Michigan	21
46	Connecticut	17
47	West Virginia	17
48	Montana	14
49	Maine	13
50	New Hampshire	12
51	Vermont	9
52	Hawaii	3

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,591
2	New Jersey	1,457
3	Connecticut	1,199
4	Massachusetts	1,144
5	Rhode Island	855
6	District of Columbia	760
7	Louisiana	674
8	Michigan	612
9	Illinois	546
10	Delaware	517
11	Maryland	511
12	Pennsylvania	508
13	Indiana	381
14	Mississippi	332
15	Colorado	289
16	Minnesota	252
17	New Hampshire	252
18	Georgia	249
19	Ohio	233
20	New Mexico	227
21	Iowa	218
22	Arizona	192
23	Virginia	192
24	Alabama	176
25	Washington	168
26	Missouri	161
27	Nevada	159
28	Florida	150
29	California	142
30	Nebraska	135
31	South Carolina	130
32	Wisconsin	128
33	Kentucky	125
34	North Carolina	121
35	North Dakota	110
36	Oklahoma	93
37	South Dakota	93
38	Kansas	89
39	Vermont	89
40	Arkansas	78
41	Tennessee	78
42	Texas	77
43	Maine	75
44	Idaho	60
45	West Virginia	51
46	Utah	50
47	Puerto Rico	46
48	Oregon	45
49	Wyoming	34
50	Montana	19
51	Alaska	13
52	Hawaii	12

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Delaware	23
2	Mississippi	5
3	Idaho	3
4	New Jersey	3
5	Rhode Island	3
6	Arizona	2
7	Connecticut	2
8	District of Columbia	2
9	Illinois	2
10	Louisiana	2
11	Maryland	2
12	Massachusetts	2
13	Nebraska	2
14	New York	2
15	Alabama	1
16	Arkansas	1
17	California	1
18	Colorado	1
19	Florida	1
20	Georgia	1
21	Indiana	1
22	Kentucky	1
23	Minnesota	1
24	New Mexico	1
25	North Carolina	1
26	Ohio	1
27	Pennsylvania	1
28	South Carolina	1
29	Virginia	1
30	Alaska	0
31	Hawaii	0
32	Iowa	0
33	Kansas	0
34	Maine	0
35	Michigan	0
36	Missouri	0
37	Montana	0
38	Nevada	0
39	New Hampshire	0
40	North Dakota	0
41	Oklahoma	0
42	Oregon	0
43	Puerto Rico	0
44	South Dakota	0
45	Tennessee	0
46	Texas	0
47	Utah	0
48	Vermont	0
49	Washington	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	130,096	1	99
Lake	Tennessee	98,204	2	99
Dakota	Nebraska	87,736	3	99
Buena Vista	Iowa	85,117	4	99
Lincoln	Arkansas	79,545	5	99
Davidson	Tennessee	11,897	225	92
Richland	South Carolina	6,725	538	82
Orange	California	3,381	1113	64
York	South Carolina	3,153	1177	62
Pierce	Washington	2,901	1262	59

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,784	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,165	3	99
Early	Georgia	3,042	4	99
Northampton	Virginia	2,391	5	99
Richland	South Carolina	192	653	79
Davidson	Tennessee	141	800	74
Pierce	Washington	103	979	68
Orange	California	87	1079	65
York	South Carolina	39	1502	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.